57 citations · 84 across the 9 of their papers we have counts for
6 papers · 1 filter
Environment Imitation: Data-Driven Environment Model Generation Using Imitation Learning for Efficient CPS Goal Verification
Yong-Jun Shin, Donghwan Shin, Doo-Hwan Bae
Cyber-Physical Systems (CPS) continuously interact with their physical environments through software controllers that observe the environments and determine actions. Engineers can…
Digital Twins Are Not Monozygotic -- Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators
Markus Borg, Raja Ben Abdessalem, Shiva Nejati +2
The increasing levels of software- and data-intensive driving automation call for an evolution of automotive software testing. As a recommended practice of the Verification and Val…
Effective Removal of Operational Log Messages: an Application to Model Inference
Donghwan Shin, Domenico Bianculli, Lionel Briand
Model inference aims to extract accurate models from the execution logs of software systems. However, in reality, logs may contain some "noise" that could deteriorate the performan…
Scalable Inference of System-level Models from Component Logs
Donghwan Shin, Salma Messaoudi, Domenico Bianculli +3
Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, th…
Empirical Evaluation of Mutation-based Test Prioritization Techniques
Donghwan Shin, Shin Yoo, Mike Papadakis +1
We propose a new test case prioritization technique that combines both mutation-based and diversity-based approaches. Our diversity-aware mutation-based technique relies on the not…
A Theoretical Framework for Understanding Mutation-Based Testing Methods
Donghwan Shin, Doo-Hwan Bae
In the field of mutation analysis, mutation is the systematic generation of mutated programs (i.e., mutants) from an original program. The concept of mutation has been widely appli…